Massive access or massive machine-type communication (mMTC) has been identified as one of the main application cases in the upcoming sixth-generation (6G) wireless networks, where the number of potential devices is expected to reach hundreds of billions by 2030. In particular, a large number of devices are connected to the Internet via a base station (BS) with sporadic communications, where only a small fraction of devices are active concurrently [1]}. In this context, applying conventional grant-based random access schemes to this emerging scenario would lead to an exceedingly high access latency and a prohibitive signaling overhead. To address this issue, grant-free random access schemes have been regarded as a promising candidate technology for enabling massive access in 6G wireless networks [2]}, where active devices transmit their data without establishing any access grant protocol with the BS. Currently, a commonly discussed category of grant-free random access is sourced random access, where each device is preassigned with a unique non-orthogonal pilot sequence and the BS identifies the device activity based on the received signals by distinguishing the transmitted sequences [1]}. However, since the number of devices in 6G wireless network grows sufficiently large, sourced random access schemes become increasingly inefficient to assign fixed pilot sequences to all potential devices. For instance, in order to detect \(K_a\)  active devices from total \(\bar{K}\)  devices, the length of pilot sequence should grow with the scale of \(K_a\log (\bar{K})\) , even through an effective sparse recovery algorithm implemented [4]}, [5]}.
